Support vector machines for spam categorization

1999/01/01 by H. Drucker, Harris Drucker, Donghui Wu +2 · 7 citations
Computer Science · #Face and Expression Recognition #Spam and Phishing Detection #Text and Document Classification Technologies

paper · doi:10.1109/72.788645

openalex publication_date 1999/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We study the use of support vector machines (SVM's) in classifying e-mail as spam or nonspam by comparing it to three other classification algorithms: Ripper, Rocchio, and boosting decision trees. These four algorithms were tested on two different data sets: one data set where the number of features were constrained to the 1000 best features and another data set where the dimensionality was over 7000. SVM's performed best when using binary features. For both data sets, boosting trees and SVM's had acceptable test performance in terms of accuracy and speed. However, SVM's had significantly less training time.

Cited by

• It's just distributed computing: Rethinking AI governance
• QuantumBoost: A lazy, yet fast, quantum algorithm for learning with weak hypotheses
• Dual-Path Phishing Detection: Integrating Transformer-Based NLP with Structural URL Analysis
• Artificial Intelligence–Based Approaches for Brain Tumor Segmentation in MRI: A Review
• Stochastic versus Deterministic in Stochastic Gradient Descent
• A Brief Survey of Text Mining: Classification, Clustering and Extraction Techniques
• Local Model Feature Transformations

Related

• Classification of Spam Emails through Hierarchical Clustering and Supervised Learning
• Machine Learning Approaches for Modeling Spammer Behavior
• Classifying spam emails using agglomerative hierarchical clustering and a topic-based approach
• Machine Learning for E-mail Spam Filtering: Review,Techniques and Trends
• Building an Effective Email Spam Classification Model with spaCy